47 research outputs found

    Online Metric-Weighted Linear Representations for Robust Visual Tracking

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    In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity comparison information and structured output learning. The learned metric is then incorporated into a linear representation of appearance. We show that online distance metric learning significantly improves the robustness of the tracker, especially on those sequences exhibiting drastic appearance changes. In order to bound growth in the number of training samples, we design a time-weighted reservoir sampling method. Moreover, we enable our tracker to automatically perform object identification during the process of object tracking, by introducing a collection of static template samples belonging to several object classes of interest. Object identification results for an entire video sequence are achieved by systematically combining the tracking information and visual recognition at each frame. Experimental results on challenging video sequences demonstrate the effectiveness of the method for both inter-frame tracking and object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and Machine Intelligenc

    Dynamic Data Mining: Methodology and Algorithms

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    Supervised data stream mining has become an important and challenging data mining task in modern organizations. The key challenges are threefold: (1) a possibly infinite number of streaming examples and time-critical analysis constraints; (2) concept drift; and (3) skewed data distributions. To address these three challenges, this thesis proposes the novel dynamic data mining (DDM) methodology by effectively applying supervised ensemble models to data stream mining. DDM can be loosely defined as categorization-organization-selection of supervised ensemble models. It is inspired by the idea that although the underlying concepts in a data stream are time-varying, their distinctions can be identified. Therefore, the models trained on the distinct concepts can be dynamically selected in order to classify incoming examples of similar concepts. First, following the general paradigm of DDM, we examine the different concept-drifting stream mining scenarios and propose corresponding effective and efficient data mining algorithms. • To address concept drift caused merely by changes of variable distributions, which we term pseudo concept drift, base models built on categorized streaming data are organized and selected in line with their corresponding variable distribution characteristics. • To address concept drift caused by changes of variable and class joint distributions, which we term true concept drift, an effective data categorization scheme is introduced. A group of working models is dynamically organized and selected for reacting to the drifting concept. Secondly, we introduce an integration stream mining framework, enabling the paradigm advocated by DDM to be widely applicable for other stream mining problems. Therefore, we are able to introduce easily six effective algorithms for mining data streams with skewed class distributions. In addition, we also introduce a new ensemble model approach for batch learning, following the same methodology. Both theoretical and empirical studies demonstrate its effectiveness. Future work would be targeted at improving the effectiveness and efficiency of the proposed algorithms. Meantime, we would explore the possibilities of using the integration framework to solve other open stream mining research problems

    Systems Analytics and Integration of Big Omics Data

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    A “genotype"" is essentially an organism's full hereditary information which is obtained from its parents. A ""phenotype"" is an organism's actual observed physical and behavioral properties. These may include traits such as morphology, size, height, eye color, metabolism, etc. One of the pressing challenges in computational and systems biology is genotype-to-phenotype prediction. This is challenging given the amount of data generated by modern Omics technologies. This “Big Data” is so large and complex that traditional data processing applications are not up to the task. Challenges arise in collection, analysis, mining, sharing, transfer, visualization, archiving, and integration of these data. In this Special Issue, there is a focus on the systems-level analysis of Omics data, recent developments in gene ontology annotation, and advances in biological pathways and network biology. The integration of Omics data with clinical and biomedical data using machine learning is explored. This Special Issue covers new methodologies in the context of gene–environment interactions, tissue-specific gene expression, and how external factors or host genetics impact the microbiome

    Genomic and Cellular Studies Establish the Pathogenesis and Cellular Mechanisms of Disease-Causing Mutations in Families with Autosomal Recessive Disorders

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    The majority of the reported genetic disorders in the UAE population are of the autosomal recessive type, which is mainly due to high rates of consanguinity within the UAE national population, and within a significant proportion of other UAE expatriate communities; such as Arabs and Pakistanis. It is estimated that more than 50% of all marriages among Emiratis occur between biologically related couples, with first cousin marriages being the highest. That could be attributed to sociocultural values in the region. Successful management of genetic diseases can be achieved by the implementation of effective preventative programs that could help reduce the number of new cases, and provide early diagnosis to potentially improve disease management. For these desired outcomes to be achieved, it is imperative to identify the molecular causes (i.e. disease-causing genes and mutations) of such disorders. Therefore, the aim of this study is to elucidate the molecular pathology and cellular mechanisms of a group of recessive disorders affecting Emirati and expatriate families in the UAE. Whole exome sequencing, together with homozygosity mapping and segregation analyses, were performed on the recruited families to elucidate the causative genes and mutations. Where necessary, bioinformatics in silico analyses coupled with cellular and other functional studies were performed to confirm pathogenicity and uncover the cellular mechanisms of the studied disease phenotypes. In this dissertation, I report the identification of two novel compound heterozygous mutations in Multiple PDZ domain (MPDZ) gene causing congenital hydrocephalus, and provide experimental evidence on their pathogenesis and mechanisms of action. In addition, I report the identification of a novel mutation in Xylosyltransferase I (XYLT1) gene responsible for Desbuquois dysplasia II (DBQDII), and provide evidence on the involvement of the endoplasmic reticulum (ER) quality control in the cellular mechanism of several DBQDII-causing mutations, including, the newly identified one. Furthermore, I provide preliminary data on candidate genes in two families affected by suspected monogenic intellectual disability syndromes. Overall, this dissertation provides evidence on the pathogenicity of several mutations and associated cellular mechanisms. The outcomes of this project will likely be valuable for implementing effective preventive strategies at least for the extended family members of the affected individuals

    On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

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    Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise
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